The effects of omeprazole on healing and appearance of small gastric and duodenal lesions during dosing with diclofenac in healthy subjects
Bibliographic record
Abstract
BACKGROUND: Non-steroidal anti-inflammatory drugs (NSAIDs) are associated with gastrointestinal mucosal damage. Omeprazole prevents the formation, and accelerates the healing, of NSAID-induced ulcers. AIM: To test whether omeprazole accelerates healing of standardized gastroduodenal lesions in the presence of diclofenac. METHODS: In a double-blind, double-dummy, placebo-controlled, crossover study, 12 healthy volunteers received consecutive, 2-week courses of omeprazole (40 mg o.d.) and placebo, in random order, with an intervening, 4-week washout period; diclofenac (50 mg t.d.s.), was given for the second week of each course. Five endoscopies were performed, one at the outset and the others before and after each course of diclofenac. Biopsies were taken from the endoscopically normal mucosa of the corpus, antrum and duodenum and also from any new mucosal lesion that developed after diclofenac. The sites of biopsies taken before each course of diclofenac were evaluated endoscopically after each course to assess the extent of healing according to a predetermined healing score scale. RESULTS: The healing scores observed after administration of placebo/diclofenac (median=0; range 0-6) and after omeprazole/diclofenac (median=0; range 0-6; P=0.17) did not differ. Small gastroduodenal lesions developed de novo in six subjects during placebo/diclofenac and in seven during omeprazole/diclofenac. Focal chemical gastropathy was observed only in close proximity to macroscopic lesions. CONCLUSIONS: In healthy subjects, omeprazole does not accelerate the healing of pre-existing mucosal lesions or prevent the development of small diclofenac-induced mucosal lesions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".